Augmented text search with syntactic information

ABSTRACT

An approach is provided in which a knowledge manager generates syntactic annotation tokens that correspond to syntactic relationships between terms included in a source document. The knowledge manager creates a knowledge structure that stores the syntactic annotation tokens in parallel fields and stores the source document terms in original text fields, which align to the parallel fields. In turn, the knowledge manager utilizes the knowledge structure to generate answers to questions based upon the syntactic annotation tokens.

BACKGROUND

System developers typically train question answer systems by ingestingcorpora from trusted, traditional sources (textbooks, journals) thatinclude accurate information. At times, a system developer may train aquestion answer system to a specific domain to increase the questionanswer system's accuracy (e.g., financial domain, travel domain, etc.).

Once the question answer system is trained, the question answer systemreceives questions and performs queries on the trained domain usingqueries such as Span Near queries, or “spannear” queries. Spannearqueries search for two words in a domain that appear close to eachother. For example, if searching for “Who is the president of CompanyABC?” the question answer system may generate “spannear(president,of)and spannear(of,Company ABC)” to search a business domain. The questionanswer system, in turn, may rank candidate answers based upon theproximity of the matched words within a sentence. For example, thesentence “John Doe is president of Company ABC” may rank higher than thesentence “John Doe replaced Sally Smith in 2010 as president of CompanyABC.”

BRIEF SUMMARY

According to one embodiment of the present disclosure, an approach isprovided in which a knowledge manager generates syntactic annotationtokens that correspond to syntactic relationships between terms includedin a source document. The knowledge manager creates a knowledgestructure that stores the syntactic annotation tokens in parallel fieldsand stores the source document terms in original text fields, whichalign to the parallel fields. In turn, the knowledge manager utilizesthe knowledge structure to generate answers to questions based upon thesyntactic annotation tokens.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present disclosurewill become apparent in the non-limiting detailed description set forthbelow.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present disclosure may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aknowledge manager system in a computer network;

FIG. 2 illustrates an information handling system, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein;

FIG. 3 is an exemplary diagram depicting a knowledge manager thatgenerates syntactic annotation tokens based upon syntactic relationsidentified within a source document and stores the syntactic annotationtokens in parallel fields within a knowledge structure to improve querysearches;

FIG. 4 is an exemplary diagram depicting a parser and syntacticannotation token generator that transforms a source document phrase intovarious types of syntactic annotation tokens;

FIG. 5 is an exemplary diagram depicting a knowledge structure thatincludes original text terms stored in original text fields andsyntactic annotation tokens stored in corresponding parallel fields;

FIG. 6 is an exemplary diagram depicting syntactic annotation tokensindexed into a knowledge structure;

FIG. 7 is an exemplary diagram depicting an embodiment of the presentdisclosure that adds abstract syntactic annotation tokens to parallelfields;

FIG. 8 is an exemplary flowchart depicting steps taken by a knowledgemanager to generate a knowledge structure that includes syntacticannotation tokens in parallel fields to enhance query searches; and

FIG. 9 is an exemplary flowchart depicting steps taken by a knowledgemanager to generate queries from a search request and search a knowledgestructure using the generated queries.

DETAILED DESCRIPTION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions. The following detailed description willgenerally follow the summary of the disclosure, as set forth above,further explaining and expanding the definitions of the various aspectsand embodiments of the disclosure as necessary.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102.Knowledge manager 100 may include a computing device 104 (comprising oneor more processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) connected tothe computer network 102. The network 102 may include multiple computingdevices 104 in communication with each other and with other devices orcomponents via one or more wired and/or wireless data communicationlinks, where each communication link may comprise one or more of wires,routers, switches, transmitters, receivers, or the like. Knowledgemanager 100 and network 102 may enable question/answer (QA) generationfunctionality for one or more content users. Other embodiments ofknowledge manager 100 may be used with components, systems, sub-systems,and/or devices other than those that are depicted herein.

Knowledge manager 100 may be configured to receive inputs from varioussources. For example, knowledge manager 100 may receive input from thenetwork 102, a corpus of electronic documents 106 or other data, acontent creator 108, content users, and other possible sources of input.In one embodiment, some or all of the inputs to knowledge manager 100may be routed through the network 102. The various computing devices 104on the network 102 may include access points for content creators andcontent users. Some of the computing devices 104 may include devices fora database storing the corpus of data. The network 102 may include localnetwork connections and remote connections in various embodiments, suchthat knowledge manager 100 may operate in environments of any size,including local and global, e.g., the Internet. Additionally, knowledgemanager 100 serves as a front-end system that can make available avariety of knowledge extracted from or represented in documents,network-accessible sources and/or structured resource sources. In thismanner, some processes populate the knowledge manager with the knowledgemanager also including input interfaces to receive knowledge requestsand respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 100. Thedocument 106 may include any file, text, article, or source of data foruse in knowledge manager 100. Content users may access knowledge manager100 via a network connection or an Internet connection to the network102, and may input questions to knowledge manager 100 that may beanswered by the content in the corpus of data. As further describedbelow, when a process evaluates a given section of a document forsemantic content, the process can use a variety of conventions to queryit from the knowledge manager. One convention is to send a well-formedquestion. Semantic content is content based on the relation betweensignifiers, such as words, phrases, signs, and symbols, and what theystand for, their denotation, or connotation. In other words, semanticcontent is content that interprets an expression, such as by usingNatural Language (NL) Processing. In one embodiment, the process sendswell-formed questions (e.g., natural language questions, etc.) to theknowledge manager. Knowledge manager 100 may interpret the question andprovide a response to the content user containing one or more answers tothe question. In some embodiments, knowledge manager 100 may provide aresponse to users in a ranked list of answers.

In some illustrative embodiments, knowledge manager 100 may be the IBMWatson™ QA system available from International Business MachinesCorporation of Armonk, N.Y., which is augmented with the mechanisms ofthe illustrative embodiments described hereafter. The IBM Watson™knowledge manager system may receive an input question which it thenparses to extract the major features of the question, that in turn arethen used to formulate queries that are applied to the corpus of data.Based on the application of the queries to the corpus of data, a set ofhypotheses, or candidate answers to the input question, are generated bylooking across the corpus of data for portions of the corpus of datathat have some potential for containing a valuable response to the inputquestion.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize knowledge manager100 range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 100. Types of computer network102 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems shown in FIG. 1 depicts separate nonvolatile data stores (server160 utilizes nonvolatile data store 165, and mainframe computer 170utilizes nonvolatile data store 175. The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates information handling system 200, more particularly, aprocessor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. Graphics controller 225 also connects to Northbridge 215. In oneembodiment, PCI Express bus 218 connects Northbridge 215 to graphicscontroller 225. Graphics controller 225 connects to display device 230,such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. The LPC busalso connects Southbridge 235 to Trusted Platform Module (TPM) 295.Other components often included in Southbridge 235 include a DirectMemory Access (DMA) controller, a Programmable Interrupt Controller(PIC), and a storage device controller, which connects Southbridge 235to nonvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 0.802.11 standards of over-the-air modulation techniquesthat all use the same protocol to wireless communicate betweeninformation handling system 200 and another computer system or device.Optical storage device 290 connects to Southbridge 235 using Serial ATA(SATA) bus 288. Serial ATA adapters and devices communicate over ahigh-speed serial link. The Serial ATA bus also connects Southbridge 235to other forms of storage devices, such as hard disk drives. Audiocircuitry 260, such as a sound card, connects to Southbridge 235 via bus258. Audio circuitry 260 also provides functionality such as audioline-in and optical digital audio in port 262, optical digital outputand headphone jack 264, internal speakers 266, and internal microphone268. Ethernet controller 270 connects to Southbridge 235 using a bus,such as the PCI or PCI Express bus. Ethernet controller 270 connectsinformation handling system 200 to a computer network, such as a LocalArea Network (LAN), the Internet, and other public and private computernetworks.

While FIG. 2 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 1.For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory.

FIGS. 3-9 depict an approach that can be executed on an informationhandling system. A knowledge manager generates syntactic annotationtokens based upon syntactic relationships between terms included in asource document. The knowledge manager generates a knowledge structurethat includes the terms in original text fields and includes thesyntactic annotation tokens in parallel fields that align to theoriginal text fields. The syntactic annotation tokens link terms thatare distant from each other and, as such, allow the information handlingsystem to perform enhanced query searches. In turn, the knowledgemanager generates a query based upon a question and uses the query tosearch the knowledge structure for answers to the question based uponthe syntactic annotation tokens.

FIG. 3 is an exemplary diagram depicting a knowledge manager thatgenerates syntactic annotation tokens based upon syntactic relationsidentified within a source document and stores the syntactic annotationtokens in parallel fields within a knowledge structure to improve querysearches.

Knowledge manager 100 includes annotation token stream generator 320,which includes parser 325 and syntactic annotation token generator 330.Knowledge manager 100 receives source documents 300 and parses thesource document using parser 325. Parser 325 may be, for example, an ESG(English Slot Grammar) parser that identifies syntactic relationshipsbetween words in phrases included in source document 300. Referring toFIG. 4, parser 325 parses phrase 400 “John gave the flowers to Mary”into six items in parsed syntactic items 410.

Syntactic annotation token generator 330 analyzes the syntacticrelationships of the syntactic items and generates syntactic annotationtokens that may be, for example, in the form of term-specific syntacticannotation tokens, variable syntactic annotation tokens, and/or relaxedsyntactic annotation tokens. In one embodiment, syntactic annotationtoken generator determines which type of syntactic annotation token togenerate based upon user preferences, which may include any or all ofthe types of above mentioned syntactic annotation tokens. For example,if the source document includes the sentence “John ate the steak”,syntactic annotation token generator 330 may generate threeterm-specific syntactic annotation tokens of subject(eat,John),object(eat,steak), and ndet(steak,the) (see FIG. 4 and correspondingtext for further details).

Knowledge manager 100 may also adapt indexing of syntactic informationto represent relationships of sets of syntactic arguments in a mannersimilar to a first-order predicate logic representation. For example, ifsource document 300 includes the sentence “John ate the steak”, theknowledge manager may construct a syntactic annotation token of“ate(John,steak)”, which enables knowledge manager 100 to identify thesyntactic relationship in the index. In one embodiment, knowledgemanager 100 may introduce variables into the relationship such as“ate(X,steak)” and “ate(John,X)”, which may be searched over withquestions such as “Who ate the steak”, “John ate this”. By introducingvariables within the syntactic annotation tokens during indexing,knowledge manager 100 is able to efficiently search over a knowledgestructure without changes to an existing search engine.

In another embodiment, knowledge manager 100 may capture the existenceof a syntactic/semantic relationship between two words even when a verbis dissimilar. For example, if knowledge manager 100 receives a questionof “When did Turkey ban YouTube” and a source document includes thesentence “Turkey blocked YouTube in 2011”, then a mismatch existsbetween the question verb and the source document sentence verb, whichresults in a degraded search. To resolve the verb mismatch, knowledgemanager 100 introduces a relaxed syntactic annotation token duringindexing such as X(Turkey,Youtube), which captures each relationshipbetween Turkey and YouTube (see FIG. 4, reference numeral 450). Inaddition, knowledge manager 100 may generalize verbs into broad classes,such as “STOP” that encompasses “ban”, “block”, “disable”, etc.

Annotation token stream generator 320 places the syntactic annotationtokens in an ordered manner (ordered syntactic annotation token map345), which are aligned with their corresponding original terms fromsource document 300 (term tokens 350). Index creator 355 indexes termtokens 350 and ordered syntactic annotation token map 345 into theirrespective original text fields 365 and parallel fields 370 to createknowledge structure 360, which is stored in knowledge base 106. Asdiscussed in more detail below, knowledge structure 360 aligns the termtokens with their respective syntactic annotation tokens for enhancedquery searches (see FIGS. 5-9 and corresponding text for furtherdetails).

Query subsystem 380 accesses knowledge structure 360 when querysubsystem 380 receives search request 370. Query subsystem 380 usessyntactic annotation token generator 385 to generate question-basedsyntactic annotation tokens from search request 375. For example, searchrequest 375 may be “Who ate the steak?” in which case syntacticannotation token generator 385 generates question-based syntacticannotation tokens of “object(eat,steak)” and “ndet(steak,the)”. Querysubsystem 380 includes the question-based syntactic annotation tokens ina query and queries knowledge structure 360 and identify candidateanswers. In turn, query subsystem 380 provides search results 390 thatinclude a list of ranked answers.

FIG. 4 is an exemplary diagram depicting a parser and syntacticannotation token generator that transforms a source document phrase intovarious types of syntactic annotation tokens. Parser 325 parses phrase400 into parsed syntactic items 410. Parser 325, in one embodiment, isan ESG parser that identifies syntactic relationships between words. Asshown in FIG. 4, parser 325 identifies six relationships that include asubject, a determiner, an object, an indirect object, an objectpreposition, and a “top” relation indicating the root word of phrase400.

Syntactic annotation token generator 330 analyzes parsed syntactic items410 and generates syntactic annotation tokens 420. Syntactic annotationtokens 420 may include term-specific syntactic annotation tokens 430,variable syntactic annotation tokens 440, and/or relaxed syntacticannotation tokens 450. Term-specific syntactic annotation tokens providehigh-precision recovery of specific syntactic relationships, whilevariable and relaxed syntactic annotation tokens provide high-recallrecovery of broad classes of syntactic relationships. The syntacticannotation tokens are then ordered and indexed into knowledge structure360's parallel fields (see FIG. 5 and corresponding text for furtherdetails).

FIG. 5 is an exemplary diagram depicting a knowledge structure thatincludes original text terms stored in original text fields andsyntactic annotation tokens stored in corresponding parallel fields.Knowledge structure 360 includes columns 500, 510, 520, and 530. Asthose skilled in the art can appreciate, knowledge structure 330 maytake on other forms besides a table, such as a data array, a database,or other type of structure that allows syntactic annotation tokens toalign with term tokens at a term position resolution.

Column 500 includes a list of term positions of original text. Theexample shown in FIG. 4 is a first sentence in a document. As such, thefirst term “John” is located at the first position in knowledgestructure 360. Column 510 includes original text fields andcorresponding term tokens (e.g., original words). Each term tokenincludes a term and term location information that indicates the termlocation in the original text stream. Column 520 includes a list ofposition increments that indicate the number of positions between theterm tokens. The embodiment in FIG. 5 shows that each of the positionincrements are “1” because a term token is stored in each original textfield position.

Column 530 includes a set of parallel fields that store the syntacticannotation tokens and align to their corresponding text token in column510. The embodiment in FIG. 5 shows that the syntactic annotation tokensare stored in parallel fields corresponding to the first word inparenthesis. For example, in position 1, subj(give,John),obj(give,flower), iobj(give,to) are all stored in the parallel fieldcorresponding to “gave.” In another embodiment, the tokens are alignedwith the second word in the pair (“John”, “flower”, and “to”,respectively).

FIG. 6 is an exemplary diagram depicting syntactic annotation tokensindexed into a knowledge structure. Parser 325 parses phrase 600 intosyntactic relationships as discussed herein, and syntactic annotationtoken generator 330 generates term-specific syntactic annotation tokens610 from the parsed syntactic relationships. As can be seen,term-specific syntactic annotation tokens 610 include separate entriesfor each of subjects “John,” “Jessie,” and “Jane”, which are allassociated with going to the park.

Index creator 355 uses an ordered annotation map generated fromterm-specific syntactic annotation tokens 610 to align the syntacticannotation tokens into their corresponding parallel fields. In oneembodiment, knowledge manager 100 includes an annotation token streamgenerator to align (index) the syntactic annotation tokens to theoriginal text stream and create an ordered annotation token map. In thisembodiment, the annotation token stream generator generates term tokensfrom the terms included in phrase 600 and uses an alignment algorithm toposition the term tokens with the corresponding syntactic annotationtokens from the unordered annotation token map. In one embodiment, theordered annotation token map is in the form of a data engine that is amachine-readable mapping organized by annotation type.

Index creator 355 indexes the term tokens into the original text fieldsbased on their position (position 1, 2, . . . ) and indexes thesyntactic annotation tokens into their respective parallel fields basedupon their aligned position assigned by annotation token streamgenerator 320. FIG. 6 shows that each of the subject syntacticannotation token subjects are stored in position 4 because “go” in thesyntactic annotation tokens corresponds to “went” in the term tokens. Assuch, query subsystem 380 provides accurate answers to a question suchas “Where did John go?” even though “John” and “park” are far apart inphrase 600.

FIG. 7 is an exemplary diagram depicting an embodiment of the presentdisclosure that adds abstract syntactic annotation tokens to parallelfields. In this embodiment, knowledge structure generator 310 analyzesphrase 700 relative to abstract concepts stored in abstract conceptsstore 710 to add abstract tokens to knowledge structure 330's parallelfields. Abstract concepts store 710 may include, for example, abstractconcept entries that associate terms that are synonyms.

FIG. 7 shows phrase 700, which is “Ginni runs Big Blue.” Knowledgestructure generator 310 may identify an abstract concept entry thatassociates “Big Blue” with “International Business Machines”. As such,knowledge structure generator creates abstract syntactic annotationtokens that replace “Big Blue” with“COMPANY_INTERNATIONAL_BUSINESS_MACHINES.” Knowledge structure 360 showsthat position 1 includes an additional syntactic annotation of“runs(Ginni, COMPANY_INTERNATIONAL_BUSINESS_MACHINES) and position 3associates Big Blue with “COMPANY_INTERNATIONAL_BUSINESS_MACHINES.” Inturn, query subsystem 380 may answer a question “Who runs Big Blue?” aswell as “Who runs International Business Machines?”

FIG. 8 is an exemplary flowchart depicting steps taken by a knowledgemanager to generate a knowledge structure that includes syntacticannotation tokens in parallel fields to enhance query searches.Processing commences at 800 whereupon at step 810, the process parses adocument and generates parsed syntactic items based upon syntacticrelationships of terms within the document.

At step 820, the process generates term-specific syntactic annotationtokens corresponding to the identified syntactic relationships, such as“subject(eat,John)” or “object(eat,steak)”. At step 830, the process, inone embodiment, generates variable syntactic annotation tokens and/orrelaxed syntactic annotation tokens based upon the term-specificsyntactic annotation tokens generated in step 820. In this embodiment,the knowledge manager may extract the same syntactic relations as instep 810 but replace each object with a variable to create variablesyntactic annotation tokens, such as “subject(eat,X)”,“subject(X,John)”, “object(X,steak)”, and “object(eat,X)”. The processmay also create relaxed syntactic annotation tokens at this step byreplacing the syntactic relationship identifiers with a variable, suchas “X(eat,John)” or “X(eat,steak).”

At step 840, the process generates an ordered syntactic annotation tokenmap that orders the syntactic annotation tokens based upon theircorresponding original terms. At step 850, the process creates aknowledge structure framework that includes original text fields and aset of parallel fields for the syntactic annotation tokens. At step 860,the process indexes the term tokens into the original text fields and,at step 870, the process indexes the ordered syntactic annotation tokentokens into the aligned parallel fields (see FIGS. 5-7 and correspondingtext for further details). FIG. 8 processing thereafter ends at 880. Inone embodiment, instead of or in addition to including a specific wordin the term-specific annotation tokens, the process replaces a word inthe syntactic annotation token with an abstract concept corresponding tothe word. For example, in the sentence “Ginni runs Big Blue”, instead ofobj(run,blue), the process may generateobj(run,COMPANY_INTERNATIONALBUSINESS_(—) MACHINES_CORPORATION).

FIG. 9 is an exemplary flowchart depicting steps taken by a knowledgemanager to generate queries from a search request and search a knowledgestructure using the generated queries. Processing commences at 900whereupon, at step 910, the process receives a search request (query,question, etc.). At step 920, the process determines a set of terms tosearch upon by removing common and uninformative words such as “the” and“an”, and, at step 930, the process parses the search request andidentifies question-based syntactic relationships between terms in thesearch request.

At step 940, the process generates question-based syntactic annotationtokens corresponding to question-based syntactic relationships betweenthe terms (term-specific syntactic annotation tokens, variable syntacticannotation tokens, relaxed syntactic annotation tokens). At step 950,the process searches the knowledge structure using the query terms andthe question-based syntactic annotation tokens. At step 960, the processperforms post-processing analysis on search results, such as ranking thesearch results and, at step 970, the process provides the search resultsto, for example, a user interface. FIG. 9 processing thereafter ends at980.

While particular embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, that changes and modifications may bemade without departing from this disclosure and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this disclosure. It will be understood by those with skill in the artthat if a specific number of an introduced claim element is intended,such intent will be explicitly recited in the claim, and in the absenceof such recitation no such limitation is present. For non-limitingexample, as an aid to understanding, the following appended claimscontain usage of the introductory phrases “at least one” and “one ormore” to introduce claim elements. However, the use of such phrasesshould not be construed to imply that the introduction of a claimelement by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim element to disclosures containingonly one such element, even when the same claim includes theintroductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an”; the same holds true for the use in theclaims of definite articles.

The invention claimed is:
 1. A method implemented by an informationhandling system that includes a memory and a processor, the methodcomprising: generating a plurality of syntactic annotation tokens basedupon a plurality of syntactic relationships between a plurality of termsincluded in a source document; creating a knowledge structure thatincludes the plurality of terms in a plurality of original text fieldsand includes the plurality of syntactic annotation tokens in a pluralityof parallel fields, wherein each of the plurality of syntacticannotation tokens align to at least one of the plurality of originaltext fields; generating one or more question-based syntactic annotationtokens corresponding to a question; and utilizing the knowledgestructure in a question answer system to generate one or more answers tothe question, wherein the question answer system matches at least one ofthe one or more question-based syntactic annotation tokens to at leastone of the plurality of syntactic annotation tokens during thegeneration of at least one of the one or more answers.
 2. The method ofclaim 1 further comprising: analyzing the question and identifying oneor more question-based syntactic relationships between question terms inthe question; generating the one or more question-based syntacticannotation tokens based upon the question-based syntactic relationships;including the question-based syntactic annotation tokens in a query; andusing the query in the querying of the knowledge structure.
 3. Themethod of claim 2 wherein the querying of the knowledge structurefurther comprises: identifying a selected one of the plurality oforiginal text fields that align to the selected parallel field; andgenerating one or more candidate answers utilizing one or more of theplurality of terms included in the selected original text field.
 4. Themethod of claim 1 wherein, during the generation of the knowledgestructure, the method further comprises: matching a selected one of theplurality of terms to an abstract concept entry, wherein the abstractconcept entry includes the selected term and a different term; for eachof the plurality of syntactic annotation tokens that include theselected term, creating an abstract syntactic annotation token thatreplaces the selected term with the different term, resulting in one ormore abstract syntactic annotation tokens; and including one or moreabstract syntactic annotation tokens in the knowledge structure.
 5. Themethod of claim 1 wherein, during the generation of the knowledgestructure, the method further comprises: selecting at least one termfrom the plurality of terms; for each of the plurality of syntacticannotation tokens that include the selected term, creating a variablesyntactic annotation token that replaces the selected term with avariable, resulting in a plurality of variable syntactic annotationtokens; and including the plurality of variable syntactic annotationtokens in the knowledge structure.
 6. The method of claim 1 wherein,during the generation of the knowledge structure, the method furthercomprises: for one or more of the plurality of syntactic annotationtokens, creating a relaxed syntactic annotation token that replaces asyntactic relationship identifier with a variable, resulting in aplurality of relaxed syntactic annotation tokens; and including theplurality of relaxed syntactic annotation tokens in the knowledgestructure.
 7. The method of claim 1 wherein, prior to the generating ofthe plurality of syntactic annotation tokens, the method furthercomprises: parsing the source document using an English Slot Grammar(ESG) parser, resulting in a plurality of syntactic items that eachidentify a syntactic relationship between a first one of the pluralityof terms and a second one of the plurality of terms, wherein theplurality of syntactic annotation tokes are generated from the pluralityof syntactic items.